Indexing, searching, and retrieving the content of spoken documents (including but not limited to recorded books, audio broadcasts, recorded conversations) is a difficult problem. Current approaches typically enable search and retrieval via the equivalent of keyword matching, either by matching a user-supplied textual query with textual metadata or by phonetic matching after transcribing the query phonetically. This approach yields low recall, i.e., many relevant speech documents may not be found for a query. Instead of keyword matching, we solve this problem by finding and retrieving spoken documents that are related to a query at the conceptual level, even if these documents do not contain the spoken (or textual) query terms.